Cartilage compositional MRI—a narrative review of technical development and clinical applications over the past three decades

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.


Introduction
Articular cartilage plays a crucial role in sustaining joint health, mobility, and function.Supported by its unique, complex structure, cartilage facilitates load transmission while providing minimal friction for articulation (Fig. 1).Cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis (OA).Cartilage imaging has transitioned from indirect imaging by radiographs to direct imaging by magnetic resonance imaging (MRI), and cartilage evaluation is now a standard component of all clinical joint MRI studies [1].Cartilage compositional MRI (Cart-C MRI) refers to quantitative MRI measurements that reflect the biochemical components and organization of the cartilage microstructure such as proteoglycan (PG), water, and collagen content, e.g., the measurement of MR relaxation times.Comparatively, quantitative morphologic imaging of cartilage (Cart-M MRI) aims to measure articular cartilage thicknesses and volumes of focal or generalized regions of the articular cartilage.The significant advances in Cart-M MRI are beyond the focus of this review [2].
Cart-C MRI began in the 1990s, aiming to detect cartilage matrix changes from injury and degeneration in the earliest stages when there is relative preservation of tissue with minimal to no visible alterations on standard MRI.Subsequently, the methods were applied to postoperative assessment of surgical cartilage repair tissue.However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles.This review will commence with an overview of the technical evolution of Cart-C MRI, including advanced sequence development, accelerated acquisition techniques, and automated analysis using artificial intelligence (AI).We will then delve into the clinical applications of Cart-C MRI, including cohort studies, clinical trials, and its use at ultra-high field strengths.The review will conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Technical developments for Cart-C MRI
Articular hyaline cartilage consists of a low density of chondrocytes that are surrounded by a large amount of extracellular matrix (ECM) composed of water in a mixture of solid components that includes proteoglycans (PGs, predominantly aggrecans in cartilage), and collagen fibers (predominantly type II in hyaline cartilage).Degeneration of cartilage is characterized by progressive breakdown of the components of the cartilage ECM, resulting from an imbalance between anabolic and catabolic processes that are predominately controlled by the chondrocytes [3].In the earliest phase of cartilage degeneration, the stage which Cart-C MRI techniques aim to detect, alterations of collagen structure and contents, loss of proteoglycan, and the consequent increase of water (tissue swelling) occur before there is cartilage thinning and tissue loss.Due to the very fast signal decay of protons in macromolecules, in vivo Cart-C MRI typically employs a strategy that "sensitizes" the measurement of relatively free water protons in the cartilage matrix to changes in macromolecular structure and content [4][5][6].Depending on the principles for this sensitization, Cart-C MRI techniques can be categorized into the following groups: (1) relaxometry; (2) diffusion imaging; (3) magnetization transfer (MT) including chemical exchange saturation transfer (CEST) imaging, and lastly (4) non-proton, sodium MRI.Table 1 summarizes the basic principles, measurements, advantages, and limitations of the major Cart-C MRI techniques.In this section, we will focus on the non-contrast relaxometry techniques, which are closest to clinical translation, and discuss recent developments including advanced sequence development, accelerated acquisition, and automated analysis.Relaxation times in the transverse plane, including T 2 , T 1ρ , and T 2 *, are sensitive to activities of the water protons restricted within the collagen-PG matrix of cartilage and can be used to detect changes in cartilage collagen and PG [22].Due to varying underlying mechanisms, each of these relaxation times has a different sensitivity to the various tissue constituents involved in cartilage degeneration.T 2 , the spin-spin relaxation, of articular cartilage is dominated by extracellular cartilage water content and collagen structure.T 2 values are modulated by the angle between collagen fibers and the magnetic field B 0 , which explains the magic angle effect of T 2 (T 2 is the longest when collagen fibers are oriented at the magic angle of 54.7°) [7].
Measurement of T 2 requires collecting multiple spin echo images with different TEs, classically 2D multi-echo spinecho (MESE) acquisitions.More recently, Carr-Purcell-Meiboom-Gill (CPMG) T 2 prepared 3D gradient echo sequences have provided more efficient image collection.Although spin echo images are in general robust to B 0 inhomogeneity, both methods can suffer from the error induced by imperfect refocusing pulse, resulting in stimulated echoes in MESE and T 1 contamination in T 2 preparation sequences.To overcome such B 1 -related error, extended phase graph (EPG) method is utilized to correct for the stimulated echo induced error in MESE [23], and more sophisticated refocusing pulse train such as Malcolm Levitt's composite pulse decoupling sequence (MLEV) is used alongside with TE correction for T 2 -prepared sequences [24].3D double-echo steady-state (DESS) sequence can generate T 2 mapping through either analytical approaches [25] or dictionary-based iterative procedures [26].Simultaneous T 1 and T 2 estimates based on triple-echo steady-state (TESS) or multiple-echo steady-state (MESS) imaging have also been developed for efficient T 1 and T 2 measurements [27,28].Currently, the 2D MESE T2 mapping techniques are available as product sequences on clinical MR systems.
T 2 * mapping has also been applied in cartilage.T 2 * is sensitive to change in T 2 as well as global and local inhomogeneity of the magnetic field.Alterations in T 2 * due to microscopic inhomogeneity can reflect tissue structural properties and provide information that is not present in T 2 measures.However, higher sensitivity to susceptibility artifacts and imperfect magnet shimming present technical challenges for T 2 * imaging and complicate data interpretation.Unlike T 2 imaging, T 2 * imaging uses gradient echoes with small flip angles (also available as product sequences on clinical MR systems) rather than spin echoes which permits fast acquisition.This allows for 3D acquisition and higher spatial resolution within a clinically relevant imaging time.However, T 2 * imaging for evaluating cartilage health is less established as both longer and shorter T2* in degenerated cartilage have been reported [29,30].Such discrepancies, caused by either different imaging protocols used or the real differences in matrix changes associated with the specific specimens/cohorts studied, present challenges when interpreting cartilage T 2 *.Combined with ultra-short TE (UTE) techniques, UTE-T2* imaging enables compositional evaluation of tissues with very short T 2 and T 2 *, such as the deep cartilage layer, menisci, ligaments, and tendons [31,32].
T 1ρ , the spin-lattice relaxation in the rotating frame, is normally measured with spin-lock (SL) techniques.Chemical exchange between protons on the protein side chain groups of glycosaminoglycan (GAG) and free water has been attributed to T 1ρ relaxation and dominate the low-frequency (0-1.5 kHz) T 1ρ dispersion [33,34].T 1ρ dispersion is the phenomenon that T 1ρ increases with SL frequency; T 2 can be considered T 1ρ at a spin-lock frequency of zero.T 1ρ has less dependence on collagen fiber orientation compared to T 2 due to reduced dipolar interactions with SL techniques.In particular, T 1ρ collagen orientation dependency is minimized when the SL frequency is higher than 2k Hz (Fig. 2) [35].For clinical imaging, the SL strength is normally limited to 500 Hz because of constraints on radiofrequency power deposition to the tissue, i.e., the allowable specific absorption rate (SAR), and hardware limitations.Therefore, clinical cartilage T 1ρ imaging is still subject to orientation dependency, although to a lesser extent than T 2 .T 1ρ measurements have shown superior to T 2 measurements in differentiating between OA patients and healthy controls, especially for early and mild OA, potentially because of better sensitivity of T 1ρ to PG loss at early stages of the disease [36][37][38][39].
T 1ρ imaging sequences are composed of T 1ρ preparation with SL pulses, followed by gradient or spin echo readout.Continuous wave (CW) SL and adiabatic SL methods have been developed.CW SL locks the magnetization with a constant RF amplitude, which defines the SL frequency.Different pulse schemes have been employed to lessen B 0 and B 1 inhomogeneity-related errors including self-compensation phase cycling of spin-lock pulses, use of refocusing pulses, composite tip down/tip up pulses, and adiabatic tip down/ tip up pulses [11,[40][41][42][43][44].Kim et al. and Pala et al. recently compared robustness to B 0 and B 1 inhomogeneity of T 1ρ preparation schemes [45,46].Adiabatic SL pulses lock the magnetization through amplitude and frequency modulated RF pulses, and is less sensitive to B 0 and B 1 inhomogeneity artifacts [47].However, the large RF energy deposition of adiabatic pulse is problematic, and the relaxation along fictitious field (RAFF) technique was developed to lessen the SAR burden [48].
T 1ρ imaging readout techniques began with 2D turbo spin echo (TSE) and spiral imaging; however, currently, 3D imaging techniques with multiple spoiled gradient echo acquisitions are most commonly used.Magnetization-prepared angle-modulated partitioned-k-space spoiled gradient-echo snapshots (termed MAPSS) T 1ρ imaging addresses several common issues during multiple gradient echo acquisitions [11,49,50].RF cycling with two acquisitions per SL is applied to eliminate the T 1 recovery contamination during the readout train.This RF cycling scheme also yields a transient signal evolution that is independent of the prepared magnetization.A variable flip angle train is then applied which provides a flat signal response and eliminates the filtering effect in k-space caused by transient signal evolution which improves quantification accuracy [11].The MAPSS acquisition has been extended to T 2 imaging with CPMG or MLEV T 2 preparation, and allows simultaneous acquisition of T 1ρ and T 2 mapping [49].More recently, Peng et al. has demonstrated that a novel unpaired phase-cycling strategy with complex-value curve-fitting can eliminate signal contaminations from T 1 recovery and allow for accurate quantitative parameter mapping with halved scan time compared to the original MAPSS strategy [51].T 1ρ dispersion experiments with multiple SL frequencies (including R 2 -R 1ρ , with R 2 as R 1ρ at SL frequency = 0) may provide more specific information regarding chemical exchange and PG concentration [52].Very recently, Han et al. proposed a novel method of using one pair T 1ρ -weighted and T 2 -weighted images to estimate R 2 -R 1ρ , with an optimal T prep considering cartilage relaxation times and image signal-to-noise ratio (SNR) [53].Currently, T 1ρ mapping sequences are only available as research prototypes on clinical MR systems.

Accelerated Cart-C MRI acquisition
Acquisition acceleration has been a crucial development in Cart-C MRI, given the typically lengthy imaging times required for obtaining multiple images for quantifying compositional parameters.
Parallel imaging techniques take advantage of multichannel coils; however, the acceleration factor (AF) is primarily limited by the number and geometry of coil channels and resulted reduced SNR in reconstructed images (note: AF indicates the scan time reduction.For example, AF = 2 reduces scan time approximately half, while AF = 3 reduces scan time approximately to 1/3 of the original time).Pakin et al. and Zuo et al. showed that a maximum AF = 3 can be achieved with parallel imaging for cartilage T 1ρ and T 2 mapping without sacrificing quantitative accuracy [54,55].
Compressed sensing (CS) techniques take advantage of image sparsity and use incoherent k-space data undersampling, which allows higher AFs [56].Huang et al. proposed to combine principal component analysis (PCA) across the temporal dimension with a model-based algorithm to reconstruct T 2 maps [57], while Peng et al. exploited the linear predictability of the T 2 exponential delay on top of the low-rank and joint-sparsity constraints [58].Both studies reported good agreement with reference maps using AF = 8 for knee cartilage.sparsifying transform and the T 1ρ images were reconstructed using dictionary learning [60].Pandit et al. combined CS with parallel imaging, where the data collected from multiple coils were used [61].Zibetti et al. compared 12 different sparsifying transforms in CS to accelerate 3D-T 1ρ imaging and suggested that spatial-temporal finite-difference (STFD) regularization had the best results [62].Using STFD CS reconstruction, Kim et al. achieved AF = 8 for simultaneous T 1ρ and T 2 imaging with standard resolution (0.44 × 0.88 × 4mm 3 ), and AF = 12 for high-resolution T 1ρ imaging (0.36 × 0.73 × 1.6mm 3 ) in a multi-vendor multi-site study, with good agreement to reference maps for both retrospective and prospective undersampling (Fig. 3) [63].
Deep learning-based reconstruction can accelerate cartilage relaxometry mapping acquisition.For example, variational networks have been used to reconstruct T 1ρ echo images from subsampled k-space data [64].The subsequently cartilage T 1ρ maps generated by monoexponential and biexponential fitting produced more accurate T 1ρ maps compared to using CS [64].Tolpadi et al. developed a recurrent U-Net pipeline with region-of-interest-specific loss functions to yield robust T 2 maps of small, clinically crucial features such as cartilage of knee and hip, and intervertebral discs, from accelerated MAPSS acquisition with AF up to 12 [65].Very recently, Li et al. demonstrated the feasibility of utilizing deep learning to accelerate T 1ρ and T 2 mapping acquisition via joint spatiotemporal undersampling (termed as superMAP).Acceleration factors as high as 32 were obtained for retrospective undersampling and AF = 26 for prospective undersampling to simultaneously reconstruct T 1ρ and T 2 maps from MAPSS combined T 1ρ and T 2 acquisition (Fig. 4) [66].With this technique, it would be possible to acquire accurate 3D T 1ρ and T 2 maps of the whole knee within 2 min, a clinically feasible acquisition time.
Fig. 3 Cartilage T 1ρ maps with accelerated imaging using compressed sensing reconstruction demonstrates excellent agreement with reference maps for both retrospective and prospective downsampling.For standard resolution imaging (top row), the average coefficient of variation (CV) between reference and AF8 maps was 2.36% and 3.07% for retrospective and prospective downsampling, respectively (n = 5).For highresolution imaging (bottom row), the average CV between reference and AF12 maps was 2.32% and 1.05% for retrospective and prospective downsampling, respectively (n = 3).AF, acceleration factor MR Fingerprinting (MRF) allows fast, simultaneous measurement of tissue properties including relaxation times for many anatomic locations [67].However, the high spatial resolution required for MSK applications is a challenge for the technique, and sequence optimizations led to promising applications of MRF in cartilage T 1 , T 2 , and T 1ρ imaging [68][69][70].The feasibility to further accelerate MRF acquisition for knee T 1 and T 2 mapping using deep learning reconstruction has been recently demonstrated [71].Compared to conventional MRF, the authors were able to reduce the number of time frames from 1000 to 50 in addition to the AF of 15 in the k-space, without sacrificing the T 1 and T 2 map quality.
Currently (n = 63).The hamstring autograft group showed significantly higher T 1ρ and T 2 in both compartments compared to the BTB autograft, suggestive of greater cartilage degeneration for this group automate cartilage segmentation for relaxometry quantification using 3D UTE-cones sequences and showed the method differentiated healthy control subjects from moderate to severe OA patients [76].
Pedoia et al. recently performed T 2 analysis on the complete OAI baseline dataset using AI [77].After a deep learning model was used for cartilage segmentation, a DenseNet directly applied to T 2 maps was used to diagnose OA and compared to the diagnosis obtained using random forest applied on demographic information and dominant principal components from voxel based relaxometry analysis.They showed that the DenseNet-based approach outperformed the conventional random forest approach in terms of ROC AUC (Fig. 6).
Schmidt et al. recently studied the generalizability of deep learning segmentation models, which can be problematic for models trained on limited image datasets [78].The authors showed that a qDESS-trained model performed better than an OAI-trained model on the independent qDESS images from four study cohorts (a total of 59 subjects and 82 knees) with various KL grades (Fig. 7), which confirmed that the domain shift problem should be considered when applying deep learning models to different cohorts.

Clinical applications of Cart-C MRI
Numerous ex vivo studies have demonstrated Cart-C MRI measures are correlated with biochemical, biomechanical, and histological analysis of cartilage specimens [79].A systematic review and meta-analysis by Emanuel et al. showed that dGEMRIC and T 1ρ are significantly correlated to PG concentrations, while T 2 had a weaker correlation with PG and showed the relationship with collagen was greater for fiber organization than collagen concentration [80].These validation studies are the foundation for proposing Cart-C MRI techniques as potential imaging biomarkers for disease.In human studies, Cart-C MRI techniques have shown good to excellent scan-rescan reproducibility.A systematic review and meta-analysis by Mackay et al. reported that for T 2 , T 1ρ , and dGEMRIC, most test/retest intraclass correlation coefficients were greater than 0.8 and coefficients of variation less than 10% [39].Based on its ex vivo validity and in vivo reproducibility, Cart-C MRI has become not only a research tool for exploring cartilage physiology, microstructure, and degeneration process, but, more importantly, a powerful instrument to provide clinically relevant evaluations [39,81].In this section, we will discuss the applications of Cart-C MRI, including cohort studies, clinical trials, Fig. 6 Diagnosing OA from T 2 maps using the entire OAI baseline cohort (n = 4384).After a deep learning model was used for cartilage segmentation, a DenseNet directly applied to T 2 maps was used to diagnose OA and compared to the diagnosis obtained using random forest applied on demographic information and dominant principal components from voxel based relaxometry analysis.A ROC curves comparing the random forest results between different feature com-bination.B ROC curves comparing the best performant shallow classifier with the deep learning model using DenseNet.DenseNet-based approach outperformed the conventional random forest approach with AUC = 0.8344.ROC, receiver operator characteristic; AUC, area under curve; PC, principal component.Figure from Reference 70 with permission patient treatment planning, and finally, Cart-C MRI at ultrahigh field.The discussion is focused on the knee, but Cart-C MRI has also been applied in other joints including the hip, ankle, hand/wrist, shoulder, and intervertebral discs [22,82].

Natural history observational cohort-the Osteoarthritis Initiative (OAI)
Cart-C MRI studies, often limited to small cohorts due to technical complexities and resource constraints, have been significantly expanded by the inclusion of T 2 mapping in the OAI protocol.Initiated in 2004, the OAI is a multicenter, prospective, observational cohort study of knee OA that collected 8 years of longitudinal imaging, including T 2 mapping, and clinical data from 4796 subjects at four sites [83].Analyses of the OAI cohort data have identified T 2 relaxation time as a marker for early-stage cartilage degeneration, as well as monitoring and predicting longitudinal disease progression [84,85].Joseph et al. showed that higher baseline cartilage T 2 values and T 2 heterogeneity were predictive of morphological degeneration of cartilage and meniscus, and bone marrow lesions over 3 years [86].Kretzschmar et al. reported that areas that developed cartilage defects over a 4-year period demonstrated elevated cartilage T 2 in the same location before the cartilage defects developed (Fig. 8) [87].Using automated cartilage segmentation with AI, Razmjoo et al. analyzed cartilage T 2 values for all OAI subjects (25,729 knee MRIs) and revealed that higher tibiofemoral T 2 values significantly increased the likelihood of developing radiographic OA and the risk of having a total knee arthroplasty [88].In addition, cartilage T 2 values have been correlated with physical activity and weight loss both cross-sectionally and longitudinally in OAI subjects [89][90][91], suggesting Arrows indicate examples of visually apparent differences in the automated segmentations and resultant T 2 maps.These differences typically appear at the periphery of cartilage surfaces, which have limited impact on subregion estimates.Figure from Reference 71 with permission that cartilage T 2 can be used to monitor outcomes for these important interventions for OA management.

Applications in sports medicine-imaging joint injuries and post-traumatic osteoarthritis
Acute joint injuries such as ACL tear, meniscal tear, patellar dislocation, intra-articular fracture, and ankle injury have shown as risk factors for post-traumatic osteoarthritis (PTOA) [92,93].Cart-C MRI provides a powerful tool for identifying the cartilage at risk for degeneration.Early identification of "cartilage at risk" with Cart-C MRI is a promising tool for the development and application of early interventions to prevent patients from progressing to permanent cartilage damage and subsequent PTOA.Cart-C MRI has been extensively studied in patients after ACL injury with a recent systematic review suggesting that T 1ρ and T 2 are promising biomarkers for diagnosis and prediction of PTOA after ACLR [94].Significantly elevated cartilage T 1ρ , T 2 , and UTE-T 2 * and decreased dGEMRIC index have been observed after acute ACL injury and after ACLR [95][96][97][98][99], compared to contralateral or control knees.Pietrosimone et al. and Williams et al. reported significant correlation between cartilage T 1ρ and UTE-T 2 * values and patient outcomes as evaluated with KOOS at 1 year and 2 years after ACLR, respectively [100,101].Using voxelbased relaxometry (VBR) analysis, Xie et al. reported that baseline T 1ρ and T 2 predicted cartilage lesion development 2 years after ACLR (Fig. 9) [102].Baseline cartilage T 1ρ and T 2 values have predicted patient outcomes evaluated by KOOS at 6, 12, and 24 months after ACLR [102,103].
A lower dGEMRIC index of femoral cartilage measured 2 years following ACL rupture was found prognostic of both radiographic and symptomatic knee OA at 14 years [104].
Using T 1ρ and T 2 as markers for the evaluation of cartilage health, researchers have identified correlations between cartilage health and factors that may contribute to PTOA development after ACL injury, including meniscal injury [105], bone shape [106], biomechanics after ACLR [107][108][109], physical activities [110], synovial fluid inflammatory biomarkers [111], and surgical factors such as time from injury to surgery, surgical technique, concomitant meniscal treatment [112,113], and quadriceps femoris strength at the time of return to sports [114].These studies suggested Cart-C MRI measures can serve as sensitive outcome measures for optimizing patient management after acute joint injuries and identifying risk factors for PTOA.

Clinical applications following cartilage repair and regeneration
Another significant application of Cart-C MRI is postoperative assessment of all types of cartilage repair surgery [115], but particularly for the determination of the structure of the generated repair tissue that grows following procedures such as microfracture, autologous chondrocyte implantation (ACI), matrix-induced ACI (MACI), and particulated cartilage allografts [116,117].MRI is an ideal noninvasive modality for determining both morphological and compositional surgical outcomes.Two systemic reviews found significant correlation between Cart-C MRI, especially T 2 mapping, and clinical outcomes following cartilage repair  regeneration in seven OA patients (KL = 3).The dGEMRIC analysis showed high GAG content in regenerated cartilage after 3 years, aligning with histological findings of the cartilage at 1 year [122].Additionally, McAlindon et al. used dGEMRIC imaging to demonstrate short-term (6 months) changes in knee hyaline cartilage following collagen hydrolysate treatment in 30 patients with mild to moderate OA [123].
In a recent phase I/IIa randomized clinical trial, Zhao et al. applied a multi-compositional MRI approach to detect changes in cartilage composition after treatment with allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs) (18 patients, KL = 2-3) [124].Significant differences were observed in quantitative T 1ρ , T 2 , T 2 *, and apparent diffusion coefficients (ADC) measurements between three dose groups, with T 1ρ being the most sensitive technique.Treated subjects showed significant improvements in WOMAC and SF-36 scores, suggesting the cartilage compositional changes may correlate with patient symptom alleviation [124].

Cart-C MRI at ultra-high field
The approval of 7 Tesla (7T) MR systems for clinical use by the European Medicines Agency (EMA) and U.S. Food and Drug Administration (FDA) in 2017 has significantly enhanced the clinical utility of 7T MRI.7T offers an increase in SNR, contrast-to-noise ratio (CNR), susceptibility effects, and spectral resolution.However, ultra-high field MRI also encounters challenges such as SAR limitations and non-uniform B 1 transmit fields.Robust shimming and RF calibration methods are even more critical for accurate and reliable MR parameter quantification.Despite these issues, Cart-C MRI at 7T continues to evolve, with expanding applications as technical issues are resolved [125].
In T 2 and T 1ρ mapping, the increased spatial resolution possible at 7T helps to alleviate partial volume averaging effects on quantitative results and improves the evaluation of thin cartilage, such as in the ankle and wrist as well as zonal comparison of deep and superficial cartilage T 2 .Domayer et al. found differences in zonal MESE T 2 assessment between the cartilage of healthy volunteers and the cartilage repair tissue of patients after two different repair techniques in the ankle joint [126].Another study reported that MESE T 2 mapping of knee cartilage can not only distinguish between the healthy cartilage and cartilage repair tissue, but also between the healthy cartilage and the cartilage adjacent to the repair tissue with otherwise normal thickness [127].Compared to MESE T 2 mapping, T 2 mapping techniques based on gradient echo acquisition, such as MAPSS and TESS, have mitigated the issue of SAR limitation and are less sensitive to B 1 inhomogeneity at 7T [128,129].Lower cartilage T 2 and T 1ρ were reported at 7T compared to 3T [130,131], as expected (T 1 increases with field strength and it is general accepted that T 2 and T 1ρ decrease with field strength but at a smaller percentage compared to T 1 increase); conversely, some investigations did not observe significant differences in cartilage between the two field strengths [128].Wyatt et al. reported an average 60% higher SNR at 7T versus 3T and found larger differences in cartilage T 2 and T 1ρ values between healthy subjects and OA patients at 7T than at 3T, suggesting greater sensitivity of T 2 and T 1ρ mapping to cartilage degeneration at 7T compared to 3T at the same resolution [128].
Glycosaminoglycan chemical exchange saturation transfer (gagCEST) greatly benefits from the improved selectivity of saturation RF pulses (because of greater frequency dispersion) at 7T as well as the improved SNR compared to 3T (Fig. 11) [132].gagCEST relies on sophisticated post-processing including motion-, B 0 -, and B 1 -corrections [132].Brinkhof et al. reported decreased gagCEST values in cartilage defects and good reproducibility when averaged over larger cartilage compartments [133].However, gagCEST maps typically show a relatively large range of values even in healthy cartilage [133] that may limit clinical utility.Furthermore, cartilage T 2 differences can introduce a pronounced bias, which may obscure the gagCEST effect when using low duty cycles and long saturation trains [134].Diffusion tensor imaging (DTI) requires sufficient spatial resolution for depiction of cartilage with relatively short T 2 and is thus limited by low SNR.Raya et al. used the greater SNR at 7T in combination with a line scan DTI sequence to show decreased fractional anisotropy (FA) and increased ADC in knee cartilage of OA patients compared to healthy volunteers [16].
In sodium ( 23 Na) MRI, direct imaging of 23 Na with MRI is difficult compared to standard proton MRI due to low tissue concentrations, inherently low SNR, very short T 2 s, and very low MR sensitivity (only 9.3% of proton MR).7T can greatly improve 23 Na MRI due to the increased SNR.Madelin et al. demonstrated 23 Na MRI was sensitive to changes in cartilage GAG content in OA patient knees over only 16 months [135]. 23Na MRI was also used to compare the quality of cartilage repair tissue 33 months after two different surgical approaches [136].A recent ex vivo 10.5T study of human pediatric knee specimens by Zbyn et al. demonstrated that 23 Na concentration and 23 Na relaxation times can non-destructively follow changes in sGAG content and collagen matrix during cartilage maturation (Fig. 12) [137].Over the past decade, significant technical advancements have been made for the first two elements, i.e., accelerated acquisition and automated analysis, particularly with the aid of deep learning-based techniques.For example, the deep learning reconstruction superMAP allows simultaneously collect cartilage T 1ρ and T 2 maps of the whole knee within 2 min [66]; the full OAI dataset has been analyzed automatically for T 2 values [88].However, the automated analysis methods have been primarily limited to homogeneous research cohort data such as OAI.Developing deep learning models that are generalizable and thus clinically useful is challenging since these images vary greatly from scanner to scanner and site to site.Furthermore, the image processing pipeline needs to be integrated into the clinical imaging workflow seamlessly before clinical implementation.For accelerated image acquisition, previous studies have been primarily limited to retrospective undersampling.More validation studies with a larger sample size of prospective undersampling and across different MR platforms are warranted before these techniques can be translated into clinical use.

Current gaps and future directions
Regarding standardization of the methodology across different MRI scanners, the MSK Quantitative Imaging Biomarker Committee (QIBC, formerly MSK committee under the Radiological Society Radiological Society of North America (RSNA)/Quantitative Imaging Biomarker Alliance [QIBA]) has assembled an expert team of radiologists, imaging researchers, clinicians (e.g., orthopedic surgeons, rheumatologists) from more than 40 institutions and involved industrial partners.The taskforce has provided recommendations pertaining to image data acquisition, analysis, and interpretation and assessment procedures for T 1ρ and T 2 cartilage imaging and test-retest conformance [139].This is a timely effort; ccMRI has demonstrated good to excellent reproducibility in single site or in multi-site single-vendor studies [39]; however, few studies have examined its reliability in a multi-site, multi-vendor context.Such reliability is crucial for its applications in clinical practice and in large-scale multi-site multi-vendor trials.One study reported that the inter-vendor mean T 2 differences ranged 5.4 to 10.0 ms (10 ~ 25%) using vendor product T 2 imaging sequences [140].Two recent studies showed that inter-vendor variations of T 1ρ and T 2 values can be reduced to approximately 10% through more harmonized sequence design and protocol setup, highlighting the importance of standardizing data acquisition [50,141].
Another fundamental question is: what additional clinical value can Cart-C MRI offer as compared to the current clinical MRI? Kijowski et al. added T 2 mapping to a routine MR protocol at 3T in 150 patients and demonstrated significantly improved sensitivity in detecting cartilage lesions with the addition of T 2 mapping [142].Beyond diagnostic utility, the potential of Cart-C MRI to enhance patient management also warrants evaluation.At the Orthopaedic Institute, University of California San Francisco (UCSF), cartilage T 1ρ mapping was integrated into the clinical routine for 390 patients (278 in knees and 112 in hips) between 2011 and 2017.A workflow was established that allowed the referring physicians to review T 1ρ maps directly from the clinical PACS (Fig. 13).A survey was conducted among six referring clinicians to evaluate the potential clinical value of T 1ρ imaging (Fig. 14).The primary indications for ordering T 1ρ mapping included preoperative planning, monitoring progression or healing of lesions, or confirming suspicious lesions that may not be seen on clinical scans.The clinicians were satisfied with the T 1ρ image quality in general, and all clinicians unanimously indicated they would like to order T 1ρ imaging to improve their patient management in the future (50% agree and 50% strongly agree).Figure 15 showed three cases where T 1ρ mapping provided useful information to clinicians for patient management.These studies offer compelling evidence that Cart-C MRI can contribute additional clinical value for both diagnosis and patient management.
Another key barrier to the clinical adoption of Cart-C MRI and other quantitative MRI techniques stems from the paucity of OA therapies and lack of any approved disease modifying OA drugs (DMOAD).Upon the approval of a DMOAD, cartilage quantitative MRI techniques should become clinically necessary for patient selection and evaluating treatment response and disease progression.Ongoing efforts to develop rapid acquisition techniques, integrate automated analysis into clinical flow, standardize acquisition and analysis protocols, and establish reference values are essential steps in preparing for this transition.

Fig. 1
Fig.1Structure and biochemistry of articular cartilage.Hyaline cartilage consists of a multi-layered structure with chondrocytes and a large extracellular matrix composed primarily of water with electrolytes, collagen fibrils, and highly negatively charged aggregates of proteoglycans (PGs).Collagen fibers (mainly type II) interact electrostatically with the glycosaminoglycans (GAGs) on PGs to form a cross-linked matrix.Biomechanically, the collagen network provides tensile stiffness to the tissue and the PG provides compressive stiffness.The collagen fibers are oriented parallel to the articular sur-

Fig. 4
Fig. 4 Cartilage T 1ρ (top) and T 2 (bottom) maps reconstructed using deep learning-based reconstruction with joint spatiotemporal undersampling (superMAP) show excellent agreement with reference maps acquired with the MAPSS sequence.With this technique, it is possible to acquire 3D T 1ρ and T 2 maps of the whole knee within 2 min.AF, acceleration factor.PSNR, peak signal to noise ratio.SSIM, structural similarity index measure.Figure revised from Reference 59 with permission

Fig. 7
Fig. 7 Comparison of manual and automatic segmentations from OAI-and qDESS-trained models and respective 2D unrolled T 2 maps in the left knee of a subject.The subject's T 2 values from the superficial and deep cartilage regions, cartilage volumes, and DSC values for the qDESS-and OAI-DESS-trained models are also shown.

Fig. 8
Fig. 8 Spatial-temporal relationship between cartilage T2 elevation and cartilage focal lesion development.A An example patient who demonstrated cartilage lesion development.While the cartilage of the lateral femoral cartilage appears normal in the sagittal fat-saturated intermediate weighted sequence (left top), local T2 elevation of the cartilage T2 map (left bottom, white arrow) is demonstrated in the lesion equivalent area 12 months prior to lesion onset (right, white arrow).B Graphic illustration of compositional cartilage degradation

Fig. 9 Fig. 10
Fig. 9 Voxel-based relaxometry (VBR) analysis demonstrated significant higher baseline (after ACL tear and before ACLR) cartilage T 1ρ values predicted cartilage lesion progression at 2 years after ACLR.A An example patient who showed patellofemoral joint (PFJ) cartilage lesion development at 2 years after ACLR (red circle).B Per-

Fig. 11
Fig. 11 Cartilage gagCEST maps in a healthy volunteer acquired at 3 T (top) and at 7 T (bottom).The gagCEST effect is much higher at 7 T compared to 3 T. Color bar shows gagCEST asymmetry values.Figure revised from Reference 127 with permission The past three decades have seen substantial advancements in Cart-C MRI.However, an important question remains: why, after 30 years of development, is Cart-C MRI not yet widely adopted in clinical practice or as a primarily outcome measures for large scale clinical trials?Link et al. previously laid out a number of essential elements that are required to apply Cart-C MRI clinically and in clinical trials[138]: (i) fast (accelerated) and

Fig. 12
Fig. 12 Ex vivo sodium MRI from the right knee of a 4.5-year-old male donor at 10.5 Tesla.A High-resolution proton 3D DESS image showing cartilage in the lateral femoral condyle, tibia, patella, and fibula.B and C Color-coded quantitative sodium ( 23 Na) maps overlaid on the 23 Na density images from the same location as the 3D DESS image.The TSC SP map corrected for specimen-specific cartilage parameters (B) showed higher concentrations compared to the TSC LB map corrected using literature-based cartilage parameters (C).D The B 1 + map.E Example fits (blue) of data points (black dots)

Fig. 13
Fig. 13 Workflow for integrating T 1ρ imaging acquisition and processing into clinical operations.Physicians ordered T 1ρ imaging through the standard ordering system (APEX).The data were collected by clinical technologists using the 3 Tesla MR scanner at the UCSF Orthopaedic Institute (OI) imaging center.T 1ρ images were automatically transferred to the clinical PACS system as all other clinical MR images.Cartilage segmentation was performed by the UCSF Radiology Quantitative Image Processing (QUIPc) group

Fig. 15
Fig. 15 Clinical utility of ccMRI.A Cartilage T 1ρ maps of a 23-yearold female obtained in August 2012 (top) and June 2013 (bottom).T 1ρ maps in June 2013 showed progressive cartilage damage in lateral femoral condyle and lateral tibia (indicated as red regions).The patient had lateral meniscal deficiency and underwent meniscus transplant in July 2013, 1 month after the bottom images were collected.B A 49-year-old male had medial cartilage damage/degeneration and meniscal tear.T 1ρ imaging (top) showing early medial compartment

Table 1
Summary of major cartilage compositional MRI techniques

Survey Results from six orthopaedic surgeons and sports dedicine primary care physicians at the UCSF Orthopaedic Institute
Survey questions (top) and results (bottom) on adding T 1ρ imaging to standard clinical MRIConclusionsThe past three decades have produced significant technical developments for cartilage evaluation.Cart-C MRI can detect early cartilage degeneration by probing changes within the collagen-PG matrix.Numerous studies have shown the potential of Cart-C MRI to serve as diagnostic, prognostic, and predictive biomarkers for diseases and injuries that impact cartilage.However, further steps are needed to bring these advanced imaging techniques into routine clinical practice and clinical trials.Further validation of accelerated imaging acquisition and automated analysis techniques, seamless integration into clinical workflows, and standardization the techniques across different MR systems will be required.With further implementation in these areas, Cart-C MRI can help to enhance preoperative planning, and monitoring progression or healing of lesions in clinical practice.In parallel, broader applications of compositional MRI in large-scale clinical trials is essential for generating sufficient data for biomarker qualification and successful DMOAD trials.The discovery and approval of successful DMOADs would undoubtedly elevate the clinical applications of Cart-C MRI.In this "chicken-egg" dilemma, synergized efforts between researchers, clinicians, and industrial partners are essential to advance the field.XL, JK, AHO, SZ, KPS, and CSW are supported by NIH/ NIAMS R01AR075422 and R01AR077452, and the Arthritis Foundation.MY is supported by NIH/NIAMS K25AR078928.KPS is also supported by NIH/NIAMS R01AR074131 and R56AR053684.TML is supported by NIH/NIAMS R01AR078917.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.